(2) Large Height Variance. In order to better verify our algorithm, we specially set a Large Height Variance subset (LHV). The separation of this subset is based on the maximum variance in pedestrian height per image. On one hand, in Figure REF , we show that the change of miss rate during training process under different settings. We found that our method can accurately detect under large height variance (LHV). On the other hand, we also integrate the algorithm with other algorithms and verify its performance on the LHV subset (Table REF ). In order to make a fair comparison, we reproduced the relevant algorithm and report a comparison between our reproduced performance and the performance reported in corresponding paper. When integrated with other algorithms, the performance has also been greatly improved, especially on the LHV subset. However, we have also noticed that although our algorithm has improved different detectors (e.g., CSP or PBM), results report in Table REF  just show competitive with the bare search algorithm in Table REF . We think this is because there exist some incompatibility between the previous algorithms and ours method. For example, PBM [1]} and BiBox [2]} both introduce part annotations, which leads to some confusion in our assignment procedure.
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